One-Bit Compressed Sensing by Greedy Algorithms

Sign truncated matching pursuit (STrMP) algorithm is presented in this paper. STrMP is a new greedy algorithm for the recovery of sparse signals from the sign measurement, which combines the principle of consistent reconstruction with orthogonal matching pursuit (OMP). The main part of STrMP is as concise as OMP and hence STrMP is simple to implement. In contrast to previous greedy algorithms for one-bit compressed sensing, STrMP only need to solve a convex and unconstraint subproblem at each iteration. Numerical experiments show that STrMP is fast and accurate for one-bit compressed sensing compared with other algorithms.Comment: 16 pages, 7 figure

The Diaspora Chinese Gospel: Pursuit of Success on Philippians 3:7-14

Traditional Western guilt-based culture evangelical tools are not effective when reaching out to non-Western people ingrained with an honor shame culture such as the Diaspora Chinese This in-depth exegesis of Philippians 3 7- 14 from the standpoint of Paul s pursuit of success along with personal testimony stories and experiences from the author s life presents a culturally relevant perspective to reach Diaspora Chinese for the Gospel The goal of Paul s pursuit was a personal relationship with Jesus Christ not worldly success The way of his pursuit was to achieve righteousness through faith in Christ The difference in emphasis between sin in a guilt-based culture and righteousness in an honor shame culture is an important distinction Paul continued his pursuit by pressing on to reach others with the message of relationship and righteousness with Christ He focused on one thing the prize his missions to reach others with the Gospel messag

Optimal Uniform Pricing Strategy of a Service Firm When Facing Two Classes of Customers

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106838/1/poms12171.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106838/2/poms12171-sup-0001-Onlinesupplement.pd

General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian

Combination of deep learning and ab initio calculation has shown great promise in revolutionizing future scientific research, but how to design neural network models incorporating a priori knowledge and symmetry requirements is a key challenging subject. Here we propose an E(3)-equivariant deep-learning framework to represent density functional theory (DFT) Hamiltonian as a function of material structure, which can naturally preserve the Euclidean symmetry even in the presence of spin-orbit coupling. Our DeepH-E3 method enables very efficient electronic-structure calculation at ab initio accuracy by learning from DFT data of small-sized structures, making routine study of large-scale supercells ($> 10^4$ atoms) feasible. Remarkably, the method can reach sub-meV prediction accuracy at high training efficiency, showing state-of-the-art performance in our experiments. The work is not only of general significance to deep-learning method development, but also creates new opportunities for materials research, such as building Moir\'e-twisted material database

Deep-learning electronic-structure calculation of magnetic superstructures

Ab initio study of magnetic superstructures (e.g., magnetic skyrmion) is indispensable to the research of novel materials but bottlenecked by its formidable computational cost. For solving the bottleneck problem, we develop a deep equivariant neural network method (named xDeepH) to represent density functional theory Hamiltonian $H_\text{DFT}$ as a function of atomic and magnetic structures and apply neural networks for efficient electronic structure calculation. Intelligence of neural networks is optimized by incorporating a priori knowledge about the important locality and symmetry properties into the method. Particularly, we design a neural-network architecture fully preserving all equivalent requirements on $H_\text{DFT}$ by the Euclidean and time-reversal symmetries ($E(3) \times \{I, T\}$), which is essential to improve method performance. High accuracy (sub-meV error) and good transferability of xDeepH are shown by systematic experiments on nanotube, spin-spiral, and Moir\'{e} magnets, and the capability of studying magnetic skyrmion is also demonstrated. The method could find promising applications in magnetic materials research and inspire development of deep-learning ab initio methods

Myo-mechanical Analysis of Isolated Skeletal Muscle

To assess the in vivo effects of therapeutic interventions for the treatment of muscle disease 1,2,3, quantitative methods are needed that measure force generation and fatigability in treated muscle. We describe a detailed approach to evaluating myo-mechanical properties in freshly explanted hindlimb muscle from the mouse. We describe the atraumatic harvest of mouse extensor digitorum longus muscle, mounting the muscle in a muscle strip myograph (Model 820MS; Danish Myo Technology), and the measurement of maximal twitch and tetanic tension, contraction time, and half-relaxation time, using a square pulse stimulator (Model S48; Grass Technologies). Using these measurements, we demonstrate the calculation of specific twitch and tetanic tension normalized to muscle cross-sectional area, the twitch-to-tetanic tension ratio, the force-frequency relationship curve and the low frequency fatigue curve 4. This analysis provides a method for quantitative comparison between therapeutic interventions in mouse models of muscle disease 1,2,3,5, as well as comparison of the effects of genetic modification on muscle function 6,7,8,9

Efficient hybrid density functional calculation by deep learning

Hybrid density functional calculation is indispensable to accurate description of electronic structure, whereas the formidable computational cost restricts its broad application. Here we develop a deep equivariant neural network method (named DeepH-hybrid) to learn the hybrid-functional Hamiltonian from self-consistent field calculations of small structures, and apply the trained neural networks for efficient electronic-structure calculation by passing the self-consistent iterations. The method is systematically checked to show high efficiency and accuracy, making the study of large-scale materials with hybrid-functional accuracy feasible. As an important application, the DeepH-hybrid method is applied to study large-supercell Moir\'{e} twisted materials, offering the first case study on how the inclusion of exact exchange affects flat bands in the magic-angle twisted bilayer graphene

Feasibility study on the preparation of artificial small blood vessel by fluorinated decellularized rabbit aorta

Objective·To explore the feasibility of fluorinated decellularized rabbit aorta as a small artificial blood vessel for tissue engineering.Methods·The obtained rabbit aorta was decellularized in combination with Triton X-100, sodium deoxycholate (SD), sodium dodecyl sulfate (SDS), DNAse, and RNAse. Hematoxylin-eosin staining (H-E staining), Masson staining and Verhoff-von Gieson staining were performed in the decellularized group and undecellularized group, respectively. The effect of decellularization was identified by field emission scanning electron microscope, and the morphological changes of decellularized blood vessels were observed. The decellularized rabbit aorta was used as the arterialized artificial small vessel scaffold, and the decellularized small vessel intima was modified with liquid perfluorocarbons coating to prepare a new type of artificial small vessel. The characteristic groups of the artificial small vessel were qualitatively and quantitatively determined. The dissipation time of liquid on the inner surface of the vessel and the flow of liquid on the surface of the vessel tilted at 45° were observed to analyze the hydrophobicity of the vessel. The blood vessels in the decellularized group and the fluorinated group were implanted with platelet-rich plasma, incubated, and observed under an electron microscope to evaluate the antiplatelet aggregation in vitro. The balloon pressure pump was connected to the aorta of the undecellularized group, decellularized group and fluoride group for bursting pressure test.Results·Histological observation of blood vessels showed that the combination could effectively remove cells while retaining collagen and elastic fibers, and there was no damage to the intima under the electron microscope. There was no significant difference in the pressure blasting test among the three groups. In the hydrophobicity experiment, the retention time of water droplets on the membrane of the fluorinated group was over 5 min, and no obvious water marks were left on the 45° inclined plate. In the platelet adhesion test, intimal aggregation activated platelets in the decellularized group, while they were inhibited in the fluorinated group.Conclusion·The decellularized blood vessels have good mechanical properties and physical stability by combined decellularization, and the fluorinated coating makes the blood vessels have good anticoagulant and biocompatibility